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Automatic Tree Detection from Three-Dimensional Images

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Automatic Tree Detection from Three-Dimensional Images remote sensing Article Automatic Tree Detection from Three-Dimensional ◦ Images Reconstructed from 360 Spherical Camera Using YOLO v2 Kenta Itakura and Fumiki Hosoi * Graduate School, University of Tokyo, Tokyo 113-8657, Japan; [email protected] * Correspondence: [email protected]; Tel.: +81-03-5841-5477 Received: 24 January 2020; Accepted: 15 March 2020; Published: 19 March 2020 Abstract: It is important to grasp the number and location of trees, and measure tree structure attributes, such as tree trunk diameter and height. The accurate measurement of these parameters will lead to efficient forest resource utilization, maintenance of trees in urban cities, and feasible afforestation planning in the future. Recently, light detection and ranging (LiDAR) has been receiving considerable attention, compared with conventional manual measurement techniques. However, it is difficult to use LiDAR for widespread applications, mainly because of the costs. We propose a method for tree measurement using 360◦ spherical cameras, which takes omnidirectional images. For the structural measurement, the three-dimensional (3D) images were reconstructed using a photogrammetric approach called structure from motion. Moreover, an automatic tree detection method from the 3D images was presented. First, the trees included in the 360◦ spherical images were detected using YOLO v2. Then, these trees were detected with the tree information obtained from the 3D images reconstructed using structure from motion algorithm. As a result, the trunk diameter and height could be accurately estimated from the 3D images. The tree detection model had an F-measure value of 0.94. This method could automatically estimate some of the structural parameters of trees and contribute to more efficient tree measurement. Keywords: automatic detection; deep learning; machine learning; spherical camera; structure from motion; three-dimensional (3D); tree trunk diameter; tree height; YOLO; 360-degree camera 1. Introduction Obtaining tree metrics has various applications in fields such as commercial forestry, ecology and horticulture. Tree structure measurement is fundamental for resource appraisal and commercial forest management [1]. In urban spaces, the knowledge of tree structure is important for their maintenance and for understanding their effects on urban environments. Trees in urban spaces have a significant influence upon urban environments through solar shading, transpiration, windbreaking, air purification and soundproofing [2]. Another important reason for measuring tree structure, including height and tree trunk diameter, is the creation of tree registries and regular monitoring for public safety. Therefore, trees in forests and cities play important roles, and the tree structural measurement is essential for their effective utilization. The conventional method is manual, and the tree measurement should be conducted one by one, meaning that the measurement is very tedious and time consuming. Besides, the manual measurement cannot be done frequently due to the hardness of the experiment. Then, measurement using terrestrial light detection and ranging (LiDAR) has attracted considerable attention. LiDAR is a three-dimensional (3D) scanner that provides highly accurate and dense 3D point clouds. A terrestrial LiDAR, which is placed on the ground, has been used widely. Remote Sens. 2020, 12, 988; doi:10.3390/rs12060988 www.mdpi.com/journal/remotesensing Remote Sens. 2020, 12, 988 2 of 20 Previous studies on the LiDAR have estimated tree trunk diameter, tree height [3–5], leaf area density [6,7], tree volume [8], biomass and leaf inclination angle [9,10]. However, when we use a LiDAR that should be stationed at one point for the measurement, it can require multiple measurements to cover the study sites, depending on the scale and shape of the study site. Recently, a light weight and portable LiDAR has been used for tree measurement [11]. This LiDAR measurement can be conducted while moving, enabling large area-measurement in a short period. Moreover, the field of view of LiDAR is large when the measurement is carried out during movement, and the maximum distance for measurement is not short (e.g., 100 m). Those LiDAR instruments also can be utilized with unmanned aerial systems or drones [12–14]. By combining LiDAR with those platforms, the 3D data of large area including numerous trees can be easily obtained. However, the instrument is costly, and the measurement process, such as in forests, is laborious. Moreover, LiDAR itself cannot obtain the color value. Structure from Motion (SfM) is a photogrammetric approach for 3D imaging, where the camera positions and orientations are solved automatically after extracting image features to recover 3D information [15]. Liang et al. [16] showed that the mapping accuracy of a plot level and an individual-tree level was 88 %, and the root mean squared error of the diameter at breast height (DBH) estimates of individual trees was 2.39 cm, which is acceptable for practical applications. Additionally, the results achieved using terrestrial LiDAR are similar. Piermattei et al. [17] concluded that the SfM approach was an accurate solution to support forest inventory for estimating the number and the location of trees. In addition, the DBHs and stem curve up to 3 m above the ground. Qiu et al. [18] developed a terrestrial photogrammetric measurement system for recovering 3D information for tree trunk diameter estimation. As a result, the SfM approach with a consumer-based, hand-held camera is considered to be an effective method for 3D tree measurement. However, the field of view of cameras is limited, compared with LiDAR [19]. For example, to capture trees on the right and left side in the direction of movement, the measurement should be conducted at least twice for both sides. Moreover, a certain distance is necessary from the target to cover the whole tree. However, it is sometimes impossible to achieve this distance because of restrictions in the field. Recently, 360◦ spherical cameras that capture images in all directions have been used widely. The field of view of the 360◦ spherical camera is omnidirectional, while that of the mobile LiDAR is not (e.g., Velodyne VLP-16: vertical 15 ). In addition, color information can be obtained as well. Moreover, it ± ◦ is much lighter and inexpensive than LiDAR. As mentioned, the 360◦ spherical camera has advantages of both a LiDAR and a camera. These are omnidirectional view, availability of color information, light weight and affordable cost. Further, 3D point clouds can be reconstructed from the spherical images using SfM [20,21]. A previous study estimated the sky view factor with the 360◦ spherical camera with color information. This sky view factor is a ratio of the sky in the upper hemisphere, and it is an important index in many environmental studies [22]. However, structural measurement via 3D imaging has not been explored thus far. Further, if structural measurement can be performed using a spherical camera, automatic tree detection, which leads to automatic tree analysis, is required. Automatic tree detection of each tree enables the estimation of properties such as tree trunk diameter and tree height. Two approaches for automatic tree detection from the spherical images can be considered. The first is to detect trees from the reconstructed 3D images using a 3D image processing technique. For example, a tree identification method with a cylinder or a circle fitting the tree trunk is used popularly [23,24]. As another method [25,26], it utilizes the intensity of the reflectance of the laser beam from the LiDAR for tree detection. However, 3D image processing requires considerable memory size, and the calculation cost tends to be large. In addition, when reconstructing 3D images using SfM, there are many missing points, owing to the absence of matching points among the input images around the missing area, and the mis-localized points. This makes it difficult to detect trees from 3D images. Remote Sens. 2020, 12, 988 3 of 20 The second approach is to detect trees in two-dimensional (2D) images and reconstruct the 3D images with the information of each tree identification. As mentioned above, if the 3D images recovered from SfM are not complete because of missing points, the resolution and accuracy of the input 2D images will be much higher than the reconstructed 3D images. Therefore, it is assumed that the detection from 2D images is more accurate than from 3D images. One of the biggest challenges in the tree detection from 2D images is the presence of numerous objects in the background. 2D image processing cannot utilize depth information, unlike 3D image processing. Recently, the methodology for detecting the target automatically has been rapidly improved, because of the use of a deep learning-based technique. A region-convolutional neural network (R-CNN) [27] applied deep learning for target detection for the first time. The traditional features are replaced by features extracted from deep convolution networks [28]. Fast R-CNN and Faster R-CNN have been developed for overcoming the disadvantages of the long processing time of R-CNN [29,30]. Nowadays, other algorithms for object detection are available, such as YOLO and SSD [31,32]. These methods operate at a very fast speed with high accuracy. The YOLO model processes images at more than 45 frames per second. The speed is increasing because the algorithm is being updated. Hence, real-time based detection is now possible. We used the YOLO v2 model [33], and the tree detector constructed based on YOLO v2 is called YO v2 detector in this paper. In this study, the tree images were obtained with a 360◦ spherical camera, and its 3D reconstruction was done using SfM to estimate the tree trunk diameter and tree height. Next, the tree detector was developed based on the YOLO v2 network. The 3D point clouds were reconstructed with information from the tree detection. Finally, the trees in the 3D point clouds were automatically detected.
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